Information processor, state judging unit and diagnostic unit, information processing method, state judging method and diagnosing method

ABSTRACT

An information processor, a state judging unit, a diagnostic unit, an information processing method, a state judging method and a diagnosing method aim at precisely recognizing each operation carried out by an object that functions in a number of operation modes. For this purpose, a number of combinations of n parameter values, concerning each of the operation modes, that vary with operation are detected by sensors ( 1   a - 1   d ) during the operation of the object, and a Self-Organizing Map creating means ( 2 ) creates Self-Organizing Maps, which corresponds one to each of the operation modes and which serve as individual separation models, regarding detection data based on the detected combination of parameter values as learning data.

TECHNICAL FIELD

The present invention relates to an information processor, a statejudging unit, a diagnostic unit, an information processing method, astate judging method and a diagnosing method for an object whichfunctions in a number of operation modes.

BACKGROUND OF THE INVENTION

In recent years, the finite resources of the earth and excessiveenvironmental burdens have lead to great need for new ways ofmaintaining machines that focus on resource circulation and reduction inenvironmental impact so that the contemporary society is converted fromexpendable to sustainable.

Conventional machine maintenance adapts corrective maintenance in whicha broken down machine is repaired or uniform preventive maintenancewhich is performed at predetermined intervals. Corrective maintenanceentails a lot of time and cost for repair. Preventive maintenancegenerates unnecessary parts and oil wastes due to its uniformity andthereby imposes greater costs on customers. Further preventivemaintenance is expensive because of the intensive labor required. Thereis a requirement for a departure from such conventional maintenancemanners and for conversion to predictive maintenance in the future.

In predictive maintenance, the degree of soundness is diagnosed byunderstanding data of load and environment during operation, a databaseof a history of past maintenance, physical failure and others andfurther deterioration and remaining life are predicted in order to finda defect on a machine at an early stage and provide a safe operationenvironment.

For example, Japanese Patent Application Laid-Open (KOKAI) No.2002-323013 (hereinafter, referred to as patent reference 1) relates toan abnormality diagnostic unit for a working vehicle such as aconstruction machine; a pressure sensor for detecting discharge pressurefrom a hydraulic pump, an engine speed sensor for detecting enginespeed, an oil temperature sensor for detecting the oil temperature in ahydraulic circuit and a communication device for radio transmittingdetection data by these sensors to a network management center (anetwork station) are installed in a vehicle body of a working machine (ahydraulic excavator) and a monitoring station (e.g., an office of themanager of the working machine) obtains the detection data of theworking machine from the network station through the Internet anddiagnoses any abnormalities of the working machine based on thedetection data.

Further, Japanese Patent Application Laid-Open (KOKAI) No. HEI 11-338848(hereinafter, referred to as patent reference 2) relates to anabnormality detection unit for a fixed machinery facility such as abatch plant or a continuous plant; normal data when the object plant isin a normal state is previously collected, on the basis of the normaldata, characteristics of the normal data are extracted using aSelf-Organizing Map; on the basis of the characteristics, acharacteristic map indicating distance relationships between outputtingunits are created and stored as a normal state model, and an abnormalityof the object plant is detected based on the normal state model andinput data (input vectors) Here, the normal state model is formed byconverting multi-dimensional data into a visualized two-dimensional mapas shown in FIG. 13 (in which the multi-dimensional data is classifiedinto five clusters expressed by regions with symbols R₁-R₅), and if theinput data has a characteristic identical to the normal state model, theinput data is judged to be normal data. The technique of patentreference 2 can totally detect an abnormality of multi-dimensional inputdata in real time.

A construction machine such as a hydraulic excavator mentioned above hasmulti-dimensional parameters (detection factors) of working pressure tocontrol the machine body moving forward and backward and slewing,working pressure of a bucket cylinder to control the bucket, workingpressure of a stick cylinder to control the stick, and working pressureof the boom cylinder to control the boom in addition to engine speed,discharge pressure from a hydraulic pump and oil temperature in ahydraulic circuit.

A construction machine carries out an operation series by combining anumber of working operations (i.e., working modes). For example, anoperation series whereby piled earth and sand are loaded onto the vessel(container) of a truck can be roughly divided into four working modes(operation modes) of “an operation from the beginning to the end ofshoveling earth and sand with the bucket (working mode 1)”, “operationof slewing the machine body to move the bucket loaded with earth andsand to the point over the vessel of the truck after shoveling earth andsand (working mode 2)”, “operation from opening the bucket to transferearth and sand to the vessel to completing the transfer (working mode3)” and “operation from returning the bucket to the piled earth and sandto being ready for operation mode 1 (working mode 4)”.

Namely, each parameter value varies with operation mode but analysis ofeach individual parameter value frequently cannot result in preciseabnormal diagnosis. For example, although each individual parametervalue is within a normal range the current working operation may nottotally correspond to any one of the above four operation modes (inmacro view). In this case, the working operation is presumed to be in anunknown operation mode or to have something wrong.

For diagnosing a machine, whether or not the current working operationconforms with one of the operation modes previously classified is judgedand, if the current working operation conforms with no operation mode,the machine is judged to be in an operation mode other than the aboveoperation modes or to have something wrong, so that it seems that theabnormality in the machine can be found more rapidly. For this reason,if all the possible operation modes of a machine of a diagnosing objectare precisely recognized in advance, an operation mode corresponding tothe current working operation can be judged in real time based onmulti-dimension parameter values.

Considering the conventional technique from this viewpoint, using theSelf-Organizing Map of patent reference 2 can classify each operationmode of the machine even if a parameter is multi-dimensional.

However, if a machine has a large number of operation modes, clusterssubstantially identical in quantity to the operation modes are formed ina single two-dimensional Self-Organizing Map, so that further increasingin quantity of operation modes reduces the area of each cluster andoverlaps between adjacent clusters is intensified to make the boundariesless clear. Such a two-dimensional map can be visually classified, butclassification requires human judgment that may not be precise. Further,if a new operation mode is to be added, the Self-Organizing Map has tobe recreated from the beginning whereupon diagnosing the machine maytake much longer.

The description so far has used the example of a construction machinebut the diagnostic unit can also be applied to many diagnosing objects(objects) whose operations (or variation of parameters) can beclassified into a number of operation modes (or variation modes).

With the foregoing problems in view, the object of the present inventionis to provide an information processor, a state judging unit, adiagnostic unit, an information processing method, a state judgingmethod and a diagnosing method for precisely recognizing each operationcarried out by an object, such as a machine, that functions in a numberof operation modes.

DISCLOSURE OF THE INVENTION

In order to solve the above problems, the present invention takes thefollowing means.

Namely, an information processor of the present invention ischaracterized by comprising: detecting means for detecting amultiplicity of combinations of n parameter values, where n is a naturalnumber, for each of a plurality of operation modes in which an objectfunctions, which values vary with operation; and Self-Organizing Mapcreating means for creating a Self-Organizing Map by using detectiondata, obtained on the basis of the multiple combinations of parametervalues detected by the detecting means, as learning data; wherein theSelf-Organizing Map creating means creates a plurality of theSelf-Organizing Maps, serving as individual separation models andcorresponding one to each of the plurality of operation modes.

An object is not only a structure that operates itself but also anentity, such as the weather, whose state varies. Additionally, aSelf-Organizing Map here does not represent only a visualizedtwo-dimensional map but shows distribution of neurons which have beentrained using learning data in a predetermined dimensional space.

With this configuration, since the Self-Organizing Map creating meanscreates Self-Organizing Maps which serve as individual separation modelsand which correspond one to each of the operation modes of the object,each operation performed by the object that functions in a number ofoperation modes can be precisely recognized.

Preferably, the detection data may be 2n-dimensional data including then parameter values, which have been detected and which indicate amomentary state of the object, and n values that are obtained bydifferentiating the n parameter values which have been detected withrespect to time and that indicate a variation in the momentary state ofthe object.

Consequently, it is possible to grasp the tendency in the datatrajectories that can be features of individual operation modes moreprecisely so that a Self-Organizing Map with higher accuracy can beobtained.

Further preferably, the detecting means may detect the multiplecombinations of n parameter values; and the Self-Organizing Map creatingmeans may initially arrange a predetermined number of neurons at randomin a 2n-dimensional space, may carry out training regarding a point(corresponding to a predetermined number of combinations (e.g., apredetermined number TD) of detection data pieces obtained based on thedetection result by the detecting means) of the detection data in the2n-dimensional space as a learning data point, may create aSelf-Organizing Map candidate regarding a neuron having a minimumdistance to the learning data point as a winning neuron, and may select,from two or more of the Self-Organizing Map candidates obtained bycarrying out the creating of a Self-Organizing Map candidate a number oftimes, a Self-Organizing Map candidate which has a characteristicclosest to that of the learning data as the Self-Organizing Map.

That results in that the selected Self-Organizing Map can be regarded asa characteristic closest to that of the learning data.

Further preferably, the Self-Organizing Map creating means may calculatean average of distances of the winning neurons to the points in thelearning data and a standard deviation of the distances of the winningneurons to the points in the learning data for each of theSelf-Organizing Map candidates, and may select a Self-Organizing Mapcandidate the average and the standard deviation of which are bothminimum as the Self-Organizing Map. Winning neurons here are all theneurons each of which has a history of being a winning neuron (in otherwords, has become a winning neuron at least once)

With this configuration, a Self-Organizing Map that characterizes thelearning data the most can be selected.

Still further, if there is no Self-Organizing Map candidate the averageand the standard deviation of which are both minimum, theSelf-Organizing Map creating means may select a Self-Organizing Mapcandidate the average of which is minimum as the Self-Organizing Map.

Further preferably, the Self-Organizing Map creating means may delete aneuron (referred to as an idling neuron) which has never become awinning neuron among neurons in the Self-Organizing Map that has beenselected.

As a result, the characteristic of the learning data can be indicated bya Self-Organizing Map the neuron number of which is greatly reduced andthe capacity for storing the Self-Organizing Map and time required forcalculation using the Self-Organizing Map can therefore be saved.

A state judging unit for judging a state of an object of the presentinvention is featured by comprising: a storage unit for storingindividual separation models in the form of the plural of theSelf-Organizing Maps, created one for each of the plurality of operationmodes by the above information processor; the detecting means; andjudging means for judging which operation mode an operation of theobject corresponds to based on a relative distance between a detectiondata point in 2n dimension corresponding to detection data obtained bythe detecting means in real time and a winning neuron in each of theplural Self-Organizing Maps. A winning neuron here is a neuron having ashortest distance a (single) data point detected in real time.

This manner can improve the accuracy of judgment for an operation modeof the object.

Preferably, the detecting means may calculate the relative distance bydividing the distance between the detection data point obtained by thedetecting means in real time and the winning neuron in each of theSelf-Organizing Maps by the average of distances of the wining neuronsin the Self-Organizing Map to the learning data point used in theprocess of creating each of the Self-Organizing Maps in the informationprocessor.

Further preferably, the judging means may judge that, if the relativedistance of each of the plural Self-Organizing Maps is equal to orsmaller than a predetermined threshold value, the detection data pointconforms with the Self-Organizing Map, and that, if the relativedistance of the one Self-Organizing Map is larger than the thresholdvalue, the detection data point does not conform with the oneSelf-Organizing Map. Further preferably, if there are two or moreconforming Self-Organizing Maps, all the conforming Self-Organizing Mapsmay be selected as candidates or the Self-Organizing Map the relativedistance of which is the minimum is selected as the best Self-OrganizingMap.

A diagnostic unit, including the above state judging unit, fordiagnosing the object, and the object may preferably be in a machineincluding a construction machine, and the plural operation modesrepresent a particular operation performed by the machine. For example,the diagnosing here is a judgment as to whether or not an operation modeof a machine or the like is normal.

This diagnostic unit can diagnose a particular operation mode of amachine or the like.

An information processing method of the present invention is featured bycomprising the steps of: detecting a multiplicity of combinations of nparameter values, where n is a natural number, for each of a pluralityof operation modes in which an object functions, which values vary withoperation; creating a Self-Organizing Map by using detection data,obtained on the basis of the multiple combinations of parameter valuesdetected in the step of detecting, as learning data; wherein, in thestep of Self-Organizing-Map creating, a plurality of the Self-OrganizingMaps, serving as individual separation models, are created one for eachof the plurality of operation modes.

Also in this method, an object is not only a structure that operatesitself but also an entity, such as the weather, whose state varies, andan operation mode includes a variation mode. Additionally, aSelf-Organizing Map is not a visualized two-dimensional map but showsdistribution of neurons which have been trained using learning data in apredetermined dimensional space.

With this method, since the Self-Organizing Map creating means createsSelf-Organizing Maps which serve as individual separation models andwhich correspond one to each of the operation modes of the object, eachoperation performed by the object that functions in a number ofoperation modes can be precisely recognized.

Preferably, the method may further comprises the step of, between thestep of detecting and the step of Self-Organizing-Map creating,calculating n time-difference values by processing the n parametervalues detected in the step of detecting, and the Self-Organizing Mapmay be created based on 2n-dimensional data including the n parametervalues, which have been detected and which indicate a momentary state ofthe object, and the n time-difference values which have been calculatedusing the n parameter values and which indicate a variation in themomentary state of the object.

Consequently, it is possible to grasp the tendency in the datatrajectories that can be features of individual operation modes moreprecisely so that a Self-Organizing Map with higher accuracy can beobtained.

Further preferably, the multiple combinations of n parameter values maybe detected in the step of detecting; and the step ofSelf-Organizing-Map creating may include the sub-steps of creating aSelf-Organizing Map candidate by initially arranging a predeterminednumber of neurons at random in a 2n-dimensional space, carrying outtraining regarding a point of the detection data in the 2n-dimensionalspace as a learning data point and creating a Self-Organizing Mapcandidate regarding a neuron having a minimum distance to the learningdata point as a winning neuron, and selecting, from two or moreSelf-Organizing Map candidates created by carrying out the step ofcreating a Self-Organizing Map candidate a number of times, aSelf-Organizing Map candidate which has a characteristic closest to thatof the learning data as the Self-Organizing Map.

In this manner, the selected Self-Organizing Map can be treated as acharacteristic closest to that of the learning data.

Still further preferably, the step of Self-Organizing-Map creatingfurther includes a sub-step of, after the sub-step of selecting aSelf-Organizing Map, deleting a neuron (i.e., an idling neuron) whichhas never become a winning neuron among neurons in the Self-OrganizingMap that has been selected.

As a result, the characteristic of the learning data can be indicated bya Self-Organizing Map the neuron number of which is greatly reduced andthe capacity for storing the Self-Organizing Map and time required forcalculation using the Self-Organizing Map can therefore be saved.

Further preferably, when a Self-Organizing Map for a new operation modeof the object other than the plural operation modes is added, thenparameter values may be detected by the step of detecting while theobject is functioning in the new operation mode by the step ofdetecting; and a Self-Organizing Map for the new operation mode may becreated regarding detection data based on a multiplicity of combinationsof the parameter values that have been detected as learning data by thestep of Self-Organizing-Map creating.

In the above manner, a Self-Organizing Map corresponding to a newoperation mode can be added.

A state judging method of the present invention for judging whichoperation mode an operation of the object corresponds to using aplurality of Self-Organizing Maps, serving as individual separationmodels and created one for each of a plurality of operation modes by theabove information processing is characterized by comprising the step of:detecting the n parameter values that vary with operation; and judgingwhich operation mode an operation of the object corresponds to based ona relative distance between a detection data point in a 2n-dimensionalspace corresponding to detection data obtained in real time in the stepof detecting and a winning neuron in each of the plural Self-OrganizingMaps.

This method can enhance the accuracy of judgment for an operation modeof the object.

Preferably, the state judgment method may further comprise the step of,between the step of detecting and the step of judging, calculating ntime-difference values by processing the n parameter values detected inthe step of detecting, and the operation mode of the object may bejudged based on 2n-dimensional data including then parameter values,which have been detected and which indicate a momentary state of theobject, and the n time-difference values, which have been processing then parameter values detected in the step of detecting and which indicatea variation in the momentary state of the object, in the step ofjudging.

Consequently, it is possible to grasp the tendency in the datatrajectories that can be features of an individual operation mode moreprecisely so that a Self-Organizing Map with higher accuracy can beobtained.

Further preferably, the step of judging may comprise: obtaining therelative distance by dividing the distance between the detection datapoint obtained in real time in the step of detecting and the winningneuron in each of the Self-Organizing Maps by the average of distancesof the wining neurons in the Self-Organizing Map to the learning datapoint used in the process of creating the Self-Organizing Map carriedout by the information processor, if the relative distance of each ofthe plural Self-Organizing Maps is equal to or smaller than apredetermined threshold value, judging the detection data point toconform with the Self-Organizing Map, and if the relative distance ofeach of the Self-Organizing Maps is larger than the threshold value,judging the detection data point not to conform with the Self-OrganizingMap. Further preferably, if there are two or more Self-Organizing Mapsconforming, all the conforming Self-Organizing Maps may be selected ascandidates or the Self-Organizing Map the relative distance of which isthe minimum is selected as the best Self-Organizing Map.

It is thereby possible to enhance the accuracy of judgment for anoperation mode of the object.

A diagnosing method of the present invention, including the above statejudging method, for diagnosing the object wherein the object is amachine including a construction machine, and the plural operation modesrepresent a particular operation performed by the machine. Thediagnosing here is a judgment as to whether or not an operation mode ofa machine or the like is normal.

With this method, a particular operation mode of a machine or the likecan be diagnosed.

Preferably, if there is no conforming Self-Organizing Map, theparticular operation may be judged to be an unknown mode or an abnormalmode in the step of judging.

This method can diagnose whether or not an operation mode of a machineor the like is an unknown mode or an abnormal mode.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a diagnostic unit according to anembodiment of the present invention;

FIG. 2 is a graph showing output values from sensors corresponding tooperation modes 1-4 of a hydraulic excavator according to an embodimentof the present invention;

FIG. 3 is a diagram visually showing the minimum distances betweenlearning data points (detection data points) and neurons in aSelf-Organizing Map according to an embodiment of the present invention;

FIG. 4(a) is a diagram explaining a Self-organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated using learning data of engine speed P₁ and left hydraulic pumppressure P₃ in operation mode 1;

FIG. 4(b) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated using learning data of engine speed P₁ and right hydraulic pumppressure P₄ in operation mode 1;

FIG. 4(c) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated using learning data of left hydraulic pump pressure P₃ and righthydraulic pump P₄ in operation mode 1;

FIG. 4(d) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated using learning data of engine speed P₁ and fuel consumptionamount P₂ in operation mode 1;

FIG. 5(a) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated using learning data of engine speed P₁ and left hydraulic pumppressure P₃ in operation mode 2;

FIG. 5(b) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated busing learning data of engine speed P₁ and right hydraulic pumppressure P₄ in operation mode 2;

FIG. 5(c) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated using learning data of left hydraulic pump pressure P₃ and righthydraulic pump P₄ in operation mode 2;

FIG. 5(d) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention, which Self-Organizing Map iscreated using learning data of engine speed P₁ and fuel consumptionamount P₂ in operation mode 2;

FIG. 6(a) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of engine speed P₁ and lefthydraulic pump pressure P₃ in operation model and neurons (larger dotsin the drawing) after complete training and deleting of idling neurons;

FIG. 6(b) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of engine speed P₁ and righthydraulic pump pressure P₄ in operation mode 1 and neurons (larger dotsin the drawing) after complete training and deleting of idling neurons;

FIG. 6(c) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of left hydraulic pumppressure P3 and right hydraulic pump pressure P₄ in operation mode 1 andneurons (larger dots in the drawing) after complete training anddeleting of idling neurons;

FIG. 6(d) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of engine speed P₁ and fuelconsumption amount P₂ in operation mode 1 and neurons (larger dots inthe drawing) after complete training and deleting of idling neurons;

FIG. 7(a) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of engine speed P₁ and lefthydraulic pump pressure P₃ in operation mode 2 and neurons (larger dotsin the drawing) after complete training and deleting of idling neurons;

FIG. 7(b) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of engine speed P₁ and righthydraulic pump pressure P₄ in operation mode 2 and neurons (larger dotsin the drawing) after complete training and deleting of idling neurons;

FIG. 7(c) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of left hydraulic pumppressure P3 and right hydraulic pump pressure P₄ in operation mode 2 andneurons (larger dots in the drawing) after complete training anddeleting of idling neurons;

FIG. 7(d) is a diagram explaining a Self-Organizing Map according to anembodiment of the present invention and showing an arrangement oflearning data (smaller dots in the drawing) of engine speed P₁ and fuelconsumption amount P₂ in operation mode 2 and neurons (larger dots inthe drawing) after complete training and deleting of idling neurons;

FIG. 8 is a diagram showing an example of a result of judgment onoperation mode according to an embodiment of the present invention;

FIG. 9 is a diagram illustrating a diagnostic unit according to amodification of the present invention;

FIG. 10 is a flow chart showing an off-line process according to anembodiment of the present invention;

FIG. 11 is a flow chart showing a process carried out in a step ofcreating a Self-Organizing Map according to an embodiment of the presentinvention;

FIG. 12 is a flow chart showing a real-time process according to anembodiment of the present invention; and

FIG. 13 is a conventional Self-Organizing Map (visualized 2-dimensionalmap).

BEST MODE FOR CARRYING OUT THE INVENTION

Hereinafter, an embodiment of the present invention will now bedescribed with reference to the accompanying drawings.

FIG. 1 is a diagnostic unit according to an embodiment of the presentinvention. The diagnostic unit is installed in a machine such as aconstruction machine to diagnose whether or not the machine hassomething wrong. Hereinafter, description will be made on the assumptionof the diagnostic unit applied to a hydraulic excavator functioning as aconstruction machine, for example. But, the present invention should byno means be applied only to such a construction machine and can beapplied to any object that is operable (variable) in a number ofoperation modes (variation modes).

As shown in FIG. 1, the diagnostic unit mainly includes a number ofsensors (detecting means) 1 a-1 d, an ECU (Electronic Control Unit) 5with functions corresponding to Self-Organizing Map creating means 2, astorage unit 3 and judging means 4, and a monitor 6. The ECU 5 includesan input/output device, a storage device (RAM, ROM) in which aprocessing program is incorporated, a central processing unit (CPU) andothers.

The sensors 1 a-1 d are prepared corresponding one to each of theparameters (variation factors) of the hydraulic excavator that isoperable in a number of operation modes, and detect a multiple ofcombinations of parameter values that vary with operation performed bythe hydraulic excavator concerning each of the operation modes. Thesesensors may directly detect corresponding parameter values or may obtaincorresponding parameter values in the form of values estimated byperforming arithmetic operations or the like on detected values.

Here, the parameters concerning operation of the hydraulic excavator arefactors that vary with operation of the hydraulic excavator and areexemplified by engine speed, fuel consumption amount, hydraulic pumppressure (pressure of one or more hydraulic pumps), oil temperature in ahydraulic circuit, working pressure to control the machine body movingforward and backward and slewing, working pressure of a bucket cylinderto control the bucket, working pressure of a stick cylinder to controlthe stick, and working pressure of the boom cylinder to control theboom.

The present diagnostic unit includes the sensors 1 a-1 d, which detectengine speed, fuel consumption amount, and hydraulic pump pressures asrepresentatives among these parameters. Specifically, the diagnosticunit includes four sensors 1 a-1 d engine speed sensor la to detect anengine speed, fuel consumption amount sensor 1 b to detect a fuelconsumption amount, and left hydraulic pump pressure sensor 1 c andright hydraulic pump pressure sensor 1 d to detect pressures of the leftand right hydraulic pumps, respectively. The diagnostic unit, of course,may include sensors to detect working pressures of the bucket cylinder,the stick cylinder, the boom cylinder and others, as mentioned above.

As one of the features of the present diagnostic unit, theSelf-Organizing Map creating means 2 creates Self-Organizing Maps(hereinafter also called SOMs) serving as separation modelscorresponding one to each operation mode of the hydraulic excavator byusing detection data based on a multiple of combinations of parametervalues detected by the engine speed sensor 1 a, the fuel consumptionamount sensor 1 b, the left hydraulic pump pressure sensor 1 c and theright hydraulic pump pressure sensor 1 d as learning data. As shown inFIG. 1, the information processor of the present invention is formed bythe sensors 1 a-1 d described above and SOM creating means 2.

Each operation mode of the hydraulic excavator represents a certainoperation (a particular operation). For example, an operation serieswhereby piled earth and sand are loaded on to the vessel (container) ofa truck can be roughly divided into four working modes (operation modes)of “an operation from the beginning to the end of shoveling earth andsand with the bucket (working mode 1)”, “operation of slewing themachine body to move the bucket loaded with earth and sand to a pointover the vessel of the truck after shoveling earth and sand (workingmode 2)”, “operation from opening the bucket to transfer earth and sandto the vessel to completing the transfer (working mode 3)” and“operation from returning the bucket to the piled earth and sand tobeing ready for operation mode 1 (working mode 4)”. The presentinvention will now be described assuming that the hydraulic excavatorfunctions in the above four operation modes.

An ordinary Self-Organizing Map is a visualized recognition model inwhich multi-dimensional data is expressed in a two-dimensional space.However, a Self-Organizing Map can be used as one method for classifyingmulti-dimensional data into the classes previously given withoutvisualizing the data in a two-dimensional space.

Description will now be made in relation to the general classification.Each data point d_(i) (i=1, 2, . . . , D) in D sets of a data cluster{d₁, d₂, . . . , d_(i), . . . , d_(D)} that have been obtained bymeasurement is formed by n parameter values (measurement characteristicvalues) which characterize a certain class C_(j) (j=1, 2, . . . , z). Inother words, each data point d_(i) is assumed to be d_(i)=[P₁, P₂, . . ., P_(n)]. A technique (a model and an algorithm associated with themodel) that can classify each data point d_(i) into a proper classsimply by reading n parameter values of the data point d_(i) is requiredfor proper classification of working modes.

This requires construction of initial knowledge based on learning datawhose “answer” is known. The “answer” means an actual class that thelearning data belongs to. Learning data is used for training a SOM(recognition model) (in other words, for gradually updating a SOM), andrepetitiously performing such training is called “supervised learning”.The SOM obtained in the above manner is used as a means for solving aclassification problem.

In construction of a SOM, using a larger amount of learning data cancreate a more precise SOM. However, once the amount of learning datareaches a certain level, further increases in data amount only slightlyimprove the precision of the SOM, so the number of inputting learningdata is preferably set to a predetermined number. The wording “class”corresponds to an “operation mode” in this embodiment.

As mentioned above, the present diagnostic unit is characterized bycreating SOMs, corresponding one to each of the operation modes of ahydraulic excavator, serving as individual separation models.

In other words, a single SOM_(j) (SOM₁, SOM₂, . . . , SOM₂) is createdfor each class C_(j) (C₁, C₂, . . . , C_(z)). Therefore, the presentembodiment creates SOMs one for each of the four classes (operationmodes). Training is performed on each SOM serving as a separation modelusing a large amount of learning data which clearly represents a singleoperation mode. Each SOM constructed by such training functions as alocal and well trained Expert that is able to clearly recognize a singleoperation mode, so that it is possible to precisely recognize each of anumber of operation modes in which an object functions.

Since one SOM learns a single operation mode and does not learn otheroperation modes, one SOM does not characterize knowledge of anotheroperation mode at the same time.

Data which is detected by four sensors 1 a-1 d and which is input to theSOM creating means 2 includes four (n) parameter values d(k) thatindicate a momentary state of the hydraulic excavator and four (n)values Δd(k) that are time-differences of the four parameter values andthat indicate a variation in the momentary state of the hydraulicexcavator, and is therefore in the form of 8-dimensional(2n-dimensional) data which totals four parameter values d(k) and 4time-differences Δd(k) of the four parameter values.

As mentioned above, the SOM creating means 2 creates a SOM based onlearning data including not only current parameter values d(k) but alsodifference values between the current parameter values d(k) and previousparameter values d(k-1), i.e., Δd(k)=d(k)−d(k-1).

Only to the current parameter values d(k) cannot obtain sufficientinformation representing dynamic operation of the entire hydraulicexcavator. But, considering also Δd(k), as mentioned above, makes itpossible to grasp more precisely the tendency of detection datatrajectories which can be features of each individual operation mode, sothat a SOM with a higher accuracy can be created.

This manner requires a longer learning time because the SOM that is tobe created is twice the data size due to data d(k) and Δd(k). It issufficient that calculation for the creation is performed only once whenthe SOM is trained, and therefore time-consuming calculations do notload the unit when the current operation mode is judged during operationof the hydraulic excavator.

The SOM creating means 2 initially arranges a predetermined number ofneurons at random in an 8-dimensional (2n-dimensional) space; carriesout training using the above learning data; creates a SOM candidateregarding a neuron having a minimum distance to the learning data pointas a winning data; and selects, from two or more SOM candidates createdby performing the above creating of a SOM candidate a number of times, aSOM candidate having a characteristic closest to that of the learningdata as a SOM.

Specifically, the SOM creating means 2 calculates the average ofdistances to learning data points and winning neurons and the standarddeviation of the distances of the learning data point to the winningneurons for each SOM candidate, and selects a SOM candidate the averageand the standard deviation of which are both minimum as a SOM. Winningneurons here are all the neurons each of which has a history of being awinning neuron (in other words, has become a winning neuron at leastonce). Further at that time, if there is no SOM candidate the averageand the standard deviation of which are both minimum, the SOM creatingmeans 2 selects a SOM candidate the average of which is minimum as theSOM.

Further, the SOM creating means 2 deletes a neuron that has never becomea winning neuron among the neurons in the selected SOM.

The training of SOMs in the above manner is preferably carried out priorto actual operation carried out by the hydraulic excavator or ispreferably carried out separately from actual operation (in thisembodiment, called the “off-line state” of the hydraulic excavator). Forexample, prior to the shipment of a hydraulic excavator from amanufacturer, the hydraulic excavator is experimentally operated alongan operation series that will be actually carried out after the shipmentand the SOM creating means 2 creates a SOM concerning each operationmode and stores the created SOMs in the storage unit 3.

While the hydraulic excavator actually functions at an operation site,the judging means 4 calculates a relative distance RD by dividing adistance between a detection data point in the 8-dimensional spacecorresponding to the detection data obtained in real time by the sensors1 a-1 d and a winning neuron (detailed later) of each SOM stored in thestorage unit 3 by the average of distances between the learning datapoints used for the process of creating the SOM by the SOM creatingmeans 2 and winning neurons in the SOM.

Further, if the relative distance RD of a SOM is equal to or smallerthan a predetermined threshold value (1+α), the judging means 4 judgesthat the detection data point conforms with the SOM and if the relativedistance RD is larger than the threshold value (1+α), the judging means4 judges that the detection data point does not conform with the SOM.The factor α represents reliability of the learning data and ispreferably given a value of 0.2-0.3, for example.

If there is a SOM conforming with the detection data point, the judgingmeans 4 judges that the detection data point belongs to an operatingmode associated with the conforming SOM. For example, if the conformingSOM is associated with the operation mode 1, the detection data point isjudged to belong to the operation mode 1. If there are two or more SOMsconforming with the detection data, the judging means 4 may select allthe conforming SOMs as candidates (in this case, may give a candidatewith a smaller relative distance a higher rank), or may select a SOMwith minimum relative distance RD as the best SOM.

Conversely, if there is no SOM conforming with the detection data point,the detection data point is judged to belong to no operation mode. Inthis case, the detection data point is judged to belong to an “unknownmode” or an “abnormal mode”. Additionally, regarding such an unknownmode as a new operation mode, the SOM creating means 2 can create a newSOM and store the new SOM in the storage unit 3.

As shown in FIG. 1, the state judging unit of the present invention isformed by the sensors 1 a-1 d, the SOM creating means 2, the storageunit 3, and the judging means 4.

The monitor 6 shows results of judgments made by the judging means 4. Inother words, if the judging means 4 judges that a detection data pointbelongs to one of the operation modes, the operation mode is displayedon the monitor 6. If the detection data point belongs to two or moreoperation modes, the operation modes may be displayed in an order ofmodes with smaller relative distances on the monitor 6. Further, if thedetection data point is judged not to correspond to any operation mode,the monitor 6 displays that the detection data point is in an unknownoperation mode (or a new operation mode) or an abnormal operation mode.

The diagnostic unit according to an embodiment of the present inventionis constructed as mentioned above, and a process for diagnosing has twomain parts of an off-line process which uses off-line data flow and areal-time process using real-time data flow.

(A) Off-Line Process:

In this process, the SOM creating means 2 creates SOMs, one for each ofthe operation modes of the hydraulic excavator, serving as separationmodels, each of which clearly indicate an associated operation mode. Theprocedure of this process uses the information processing methodaccording to this embodiment including the steps of detecting for datacreation (step S100), calculating (S110), and creating a SOM (step S120)as shown in FIG. 10.

The step of detecting for data creation (step S100) obtains a largeamount of detection data with high reliability for each of the operationmodes of the hydraulic excavator. Specifically, in the presentembodiment, a multiple of combinations of parameter values of eachoperation mode are obtained from the four sensors 1 a-1 d. Here, aparameter value at current time k is represented by d(k).

In the step of calculating (step S110), the parameter values detected inthe step of detecting for data creation are processed to calculate timedifferences (including values corresponding to time differences such asvariation rates of the parameter values (e.g., variation amounts perunit time such as a detection period or detection cycle)) Δd(k).

In the step of creating a SOM (step S120), a SOM, which is regarded as aseparation model of each operation mode, is created using detection data{d(k), Δd(k)} based on the multiple combinations of parameter valuesd(k) obtained in the step of detecting for data creation and themultiple combinations of time-differences Δd(k) calculated in the stepof calculating as learning data.

This off-line process requires time but is the most important steps thatdetermine the quality of the SOM that is to be used for classificationcarried out in the later real-time process.

FIG. 2 shows parameter values of the sensors 1 a-1 d when the hydraulicexcavator repetitiously performs an operation series of the operationmodes 1 through 4 and the horizontal axis represents a common timescale. The graphs respectively indicate engine speed, fuel consumptionamount, left pump pressure, right pump pressure, and variation inoperation mode from the top. As can be understood from FIG. 2, obtainingthe same parameter values in the same operation mode (waveforms) isideal but actual parameter values may be different even in the sameoperation modes. Therefore, training a SOM using a large amount ofreliable learning data in this off-line process can create a SOM thatcharacterizes each operation mode more clearly.

The above manner obtains a typical SOM for each operation mode. Theconcept of the training has the following feature. Since each SOM istrained concerning only one operation mode, there is no requirement forshowing topological distances (vicinity, neighborhood) of neurons on agraph of a two-dimensional map expressed by using software of SOM knownto the public. Obtaining a distribution (called “cloud” here) of neuronsin an 8-dimensional space is sufficient for the SOM of the presentembodiment.

Next, description will now be made in relation to a specific calculationprocess carried out in the step of creating a SOM.

First of all, a predetermined number of neurons are arranged at randomin the 8-diemnsional space (step S200, the first step), as shown in FIG.11. For each of the detection data points (regarded as learning data forcreation of a SOM in the off-line process) in the 8-dimensional space,distances to the neurons are obtained (step S210). After that, theneuron having the minimum distance to the detection data point isdetermined to be a winning neuron. At the same time, not only thewinning neuron but also neurons in the vicinity of the winning neuronare trained.

Here, the minimum distance MD is defined as the minimum value among thedistances of the i-th detection data point to the neurons in a2n-dimensional space. For example, if the distance to the j-th neuron isthe minimum, the j-th neuron with the minimum distance is called thewinning neuron. The minimum distance MD is expressed by the followingformula (1); $\begin{matrix}{{{{MD}(i)} = {\min\limits_{1 \leq j \leq n}\{ {r( {i,j} )} \}}}{{where},{i = 1},2,\ldots\quad,{{TD}.}}} & (1)\end{matrix}$

Here, r(i, j) represents the distance between the i-th detection datapoint and the j-th neuron. Further, the distance r(i, j) is calculatedto be an Euclidean distance as known in an ordinary algorithms for aSOM. TD represents the number of (combinations of) learning data pieces.

After that, whether or not a SOM is trained using all the multiple ofcombinations is judged (step S230), and if the result of the judgment isnegative (No judgment), the process shifts to step S210. On the otherhand, if the result of the judgment is positive (Yes judgment), theprocess shifts to step S240 to create another SOM candidate. The SOMobtained at this stage can not always be the best SOM that definitelyindicates a single operation mode and is therefore treated as acandidate. The steps S210 through S240 are the second step and the stepof creating a SOM candidate is formed by the first and the second steps.

The above calculation process has created a SOM candidate for a certainoperation mode. In the present embodiment, in order to obtain the bestSOM with higher accuracy that expresses the feature of the operationmode more clearly, a number of SOM candidates are created, from whichthe best SOM is selected. For this purpose, whether or not the number ofcreated SOM candidates reaches the number predetermined before thecreation of a SOM is judged. If the result is No, the process shifts tostep S200 to create another SOM candidate and conversely, if the resultis Yes, the process shifts to step S260.

In step S260 (a step of selecting), one SOM candidate having acharacteristic closest to that of the learning data is selected from theSOM candidates as a SOM. Here, the manner for selecting a best SOM instep S260 will now be detailed.

Important index values to characterize the distribution of neurons in a2n-dimensional space are an average minimum distance AV_(min) and thestandard deviation ST_(dev) of the minimum distances MD.

FIG. 3 is an example that visually indicates the minimum distancesbetween ten detection data points (referred to as learning data pointsin FIG. 3 because detection data points are regarded as learning data inthe off-line process) d₁-d₁₀ and seven neurons n₁-n₇. The averageminimum distance AV_(min) is the average of these minimum distances MD.The average minimum distance AV_(min) is expressed by the followingknown formula (2); $\begin{matrix}{{AV}_{\min} = {\frac{1}{TD}{\sum\limits_{i = 1}^{TD}{{MD}(i)}}}} & (2)\end{matrix}$

Similar to the formula for the average minimum distance AV_(min),standard deviation ST_(dev) is obtained by the following known formula(3); $\begin{matrix}{{ST}_{dev} = \sqrt{\frac{\sum\limits_{i = 1}^{TD}( {{{MD}(i)} - {AV}_{\min}} )}{TD}}} & (3)\end{matrix}$

On the basis of the average minimum distance AV_(min) and the standarddeviation ST_(dev), the step S260 judges which SOM has a characteristicclosest to that of the learning data among a number of SOMs that havebeen calculated to be candidates. At that time, a SOM candidate, theaverage minimum distance AV_(min) and the standard deviation ST_(dev) ofwhich are both minimum, is selected as the best SOM that is the closestto the learning data characteristic.

If there is no SOM candidate the average minimum distance AV_(min) andthe standard deviation ST_(dev) of which are both minimum, a SOMcandidate the average minimum distance AV_(min) of which is minimum isselected as the best SOM.

In this manner, it is possible to select a SOM that is the mostcharacteristic of the detection data (learning data).

In step S270 (a step of deleting an idling neuron), one or more neurons(called “idling neurons” here) that have never become winning neurons inthe selected SOM are deleted. For example, FIG. 3 shows two idlingneurons n₃ and n₇, which are deleted after training the SOM. Applicationof such elimination of an idling neuron can express the learning datacharacteristic in terms of a SOM in which the number of neurons isgreatly reduced, so that the capacity for retaining the SOM can be savedand the time required for future calculation using the SOM can bereduced.

As described in this embodiment, the merits of the use of one SOM (aseparation model) for one operation mode are that the storage capacitycan be greatly reduced by approximating an enormous number of detectiondata points that characterize the operation mode to neurons, the numberof which are greatly reduced, and that classification carried out in thesubsequent real-time process can be rapidly executed.

FIGS. 4(a), 4(b), 4(c) and 4(d) are graphs of detection data points inthe operation mode 1; FIG. 4(a) shows the relationship between theengine speed P₁ and the left hydraulic pump pressure P₃; FIG. 4(b) showsthe relationship between the engine speed P₁ and the right hydraulicpump pressure P₄; FIG. 4(c) shows the relationship between the lefthydraulic pump pressure P₃ and the right hydraulic pump pressure P₄; andFIG. 4(d) shows the relationship between the engine speed P₁ and thefuel consumption amount P₂. Since the SOMs (separation models) of FIGS.4(a), 4(b), 4(c) and 4(d) are eight dimensional, the SOMs are in theform of maps in which winning neurons are arranged in aneight-dimensional space.

FIGS. 5(a), 5(b), 5(c) and 5(d) are graphs of detection data points inthe operation mode 2. Since the SOMs (separation models) of FIGS. 5(a),5(b), 5(c) and 5(d) are also eight dimensional, the SOMs are in the formof maps in which neurons are arranged in an eight-dimensional space.

FIGS. 6(a), 6(b), 6(c) and 6(d) show the best SOMs concerning theoperation mode 1 that are to be used in the subsequent real-timeprocess. The smaller dots in FIGS. 6(a) 6(b), 6(c) and 6(d) are thedetection data points in the operation mode 1 and the larger dots areneurons after the complete training and deletion of idling neurons havebeen carried out.

Similarly, FIGS. 7(a), 7(b), 7(c) and 7(d) show the best SOMs concerningthe operation mode 2 that are to be used in the subsequent real-timeprocess. The smaller dots in FIGS. 7(a), 7(b), 7(c) and 7(d) are thedetection data points in the operation mode 2 and the larger dots areneurons after the complete training and deletion of idling neurons havebeen carried out.

From FIGS. 6(a), 6(b), 6(c), 6(d), 7(a), 7(b), 7(c) and 7(d), it isobvious that neurons are mainly arranged in the regions with the highestdensity.

These neurons are used as the representative points of the entiredetection data points in the subsequent real-time process.

(B) Real-Time Process:

This process judges which operation mode the hydraulic excavator iscurrently functioning in, on the basis of the detection data obtained inreal time by the hydraulic excavator which is actually functioning.Specifically, calculation is carried out in order to judge which SOMamong the four SOMs created in the off-line process described above thereal-time detection data obtained here is the most similar to, so thatthe operation mode corresponding to the SOM that is most similar isdetermined. The state judging method and the diagnosing method accordingto this embodiment are used for the procedural steps of this process.

As shown in FIG. 12, four parameter values, i.e., detection data, aredetected in real time at first (step S300, a step of detecting for statejudging). The parameter values detected in step S300 are processed tocalculate time differences (including values corresponding to timedifferences, (such as variation rates of parameter values (e.g.,variation amounts per unit time exemplified by detection period time))Ad(k) of the parameter values (step S310, a step of calculating).Namely, the detection data is eight-dimensional data including four d(k)and four Δd(k) similar to data in the off-line process.

Next, the similarity degrees SDs of the current detection data to SOMs,one concerning each of the operation modes, are obtained. There are anumber of manners to calculate a similarity degree SD, but the presentembodiment obtains similarity degrees SDs by using Euclidean distance,i.e., distance of a current detection data point to a winning neuron ina SOM.

The similarity degree thus obtained is divided by the average minimumdistance AV_(min) to thereby obtain the relative distance RD(=SD/AV_(min)) between the current detection data point and the winningneuron. The winning neuron here is a neuron having the shortest distanceto a data point (a single point) detected in real time. The calculationfor a relative distance RD is performed on each of the four SOMs (stepS320).

Whether or not the relative distance RD that has been calculated asabove is equal to or smaller than a predetermined value (1+α), i.e.,whether or not RD≦1+α (α is a threshold value previously determined) isjudged (step S330). If the relative distance is equal to or less thanthe predetermined value, the detection data point is judged to conformwith the SOM and the SOM is stored in a storage unit to be a candidate(step S340). In other words, this means that the above detection datapoint can be classified into an operation mode associated with theconforming SOM.

Conversely, if the relative distance RD is (equal to or) larger than thepredetermined value, the detection data point is judged not to conformwith the SOMs (step S350). In other words, that means that the abovedetection data point cannot be classified into any operation modes. Thesteps S320-S340 are the step of judging. Appropriate setting of theabove predetermined value (1+α) can determine a criterion for judging asto whether or not a detection data point conforms to a SOM in accordancewith the circumstances.

The above judgment is performed on SOMs for the four operation modes,and if there are two or more SOMs conforming with a detection data point(i.e., there are two or more operation modes conforming), the operationmodes corresponding to the SOMs are notified to an operator via themonitor 6. In this case, the operation modes are displayed in order ofsmaller relative distances, i.e., in order of higher similarity degree,so that the operator easily grasps the display of the operation modes.

If there is no SOM that conforms with the detection data point (i.e.,there is no operation mode conforming), the operator is notified thatthe detection data point is in an “unknown operation mode” that has notbeen trained in the off-line process or in an “abnormal mode” via themonitor 6. Such a display of the presence or the absence of abnormalityon the hydraulic excavator currently functioning can issue a kind ofalert to the operator.

One of the characteristics of the real-time process according to thepresent operation is adaptability. Specifically, if the operator of thehydraulic excavator operates the hydraulic excavator in a new operationmode, detection data concerning only the new operation mode is obtainedand is subjected to training, so that a new SOM_(z+1) can be created.The new SOM_(z+1) can be added to the SOMs that have been already used.In other words, the off-line process in this embodiment has created thefour SOMs, corresponding one to each of the four operation modes; if thenew SOM is added, the present embodiment creates and stores five SOMs intotal.

As mentioned above, if addition of a new SOM is intended, the entiremodel for classification can be updated with ease simply by adding theSOM_(z+1) serving as a new separation model to the entire model. Forthis reason, there is no requirement for recreation of the entire model(i.e., an entire conventional visualized two-dimensional map) forclassification from the beginning which conventional creation techniqueshave required. Adding new a operation mode at any time in this mannercan diagnose each operation more precisely.

FIG. 8 shows an example of the judgment result of operation mode made bythe diagnostic unit according to this embodiment. In FIG. 8, the solidline indicates the actual operation modes of the hydraulic excavator,and the broken line indicates operation modes classified using SOMs. Inthe present embodiment, operation modes that have previously trained inthe off-line process are the operation mode “1”, the operation mode “2”,the operation mode “3” and the operation mode “4”, and an operation mode(e.g., a mode in which the hydraulic excavator is idling) that has notbeen previously trained is operation mode “0”. An operation mode “−1”indicates an unknown mode or an abnormal mode.

As can be seen from FIG. 8, although an operation mode is sometimesjudged to be an operation different from the actual operation mode,correct judgment of operation mode substantially in coordination withthe actual operation modes is carried out.

One embodiment of the present invention has been described above, butthe present invention should by no means be limited to the foregoingembodiment and various modifications can be suggested without departingfrom the concept of the present invention.

For example, the present embodiment uses the detection data of d(k) andΔd(k) without processing. Alternatively, these values may not bedirectly used but may be used after being subjected to smoothing carriedout by a primary filter.

The number of neurons that is used for creation of a SOM may beincreased of course although calculation will require longer time. Inthis manner, a more precise SOM can be created.

Further, the present embodiment has made description regarding ahydraulic excavator as an example of an object that functions in anumber of operation modes. But, the object should not be limited to ahydraulic excavator only. The present invention can also be applied toright-wrong judgment of operations performed by a conveyance such as atruck, a bus or a vessel or by machines such as an industrial machine,and also applied to right-wrong judgment of living organisms such asanimals or plants and to estimation of changes in weather or in acelestial body such as the earth.

In this embodiment, the diagnostic unit is installed in the hydraulicexcavator and the diagnosing process is carried out in the hydraulicexcavator in a lump. Alternatively, as shown in FIG. 9, only sensors areinstalled in a mobile machine such as a hydraulic excavator and acomputer including the SOM creating means 2, the storage unit 3, thejudging means 4 and the monitor 6 described in the present embodiment isinstalled in a business entity, so that a diagnosing can be carried outwith ease at the business entity by sending detection data from thesensors to the computer and displaying the sent data on the computereven if it is remote from the mobile machine. Further, the example shownin FIG. 9 interposes a management system between mobile machines andbusiness entities. In particular, if an object is a mobile machine suchas a construction machine, a truck, a bus or a vessel, the configurationof the diagnostic unit according to the present invention can fulfillthe demands for higher maintenance and higher efficiency for maintenancefor reasons of inefficiency due to geometric distribution.

INDUSTRIAL APPLICABILITY

As described above, since the present invention can precisely recognizeeach operation of an object, such as a construction machine, that isable to function in a number of operation modes if applied to theobject, the usability of the present invention is considered to beextremely high.

1. An information processor comprising: detecting means for detecting amultiplicity of combinations of n parameter values, where n is a naturalnumber, for each of a plurality of operation modes in which an objectfunctions, which values vary with operation; and Self-Organizing Mapcreating means for creating a Self-Organizing Map by using detectiondata, obtained on the basis of the multiple combinations of parametervalues detected by said detecting means, as learning data; wherein saidSelf-Organizing Map creating means creates a plurality of theSelf-Organizing Maps, serving as individual separation models andcorresponding one to each of the plurality of operation modes.
 2. Aninformation processor according to claim 1, wherein the detection datais 2n-dimensional data including the n parameter values, which have beendetected and which indicate a momentary state of the object, and nvalues that are obtained by differentiating the n parameter values whichhave been detected with respect to time and that indicate a variation inthe momentary state of the object.
 3. An information processor accordingto claim 2, wherein said detecting means detects the multiplecombinations of n parameter values; and said Self-Organizing Mapcreating means initially arranges a predetermined number of neurons atrandom in a 2n-dimensional space, carries out training regarding a pointof the detection data in the 2n-dimensional space as a learning datapoint, creates a Self-Organizing Map candidate regarding a neuron havinga minimum distance to the learning data point as a winning neuron, andselects, from two or more of the Self-Organizing Map candidates obtainedby carrying out the creating of a Self-Organizing Map candidate a numberof times, a Self-Organizing Map candidate which has a characteristicclosest to that of the learning data as the Self-Organizing Map.
 4. Aninformation processor according to claim 3, said Self-Organizing Mapcreating means calculates an average of distances of the winning neuronsto the points in the learning data and a standard deviation of thedistances of the winning neurons to the points in the learning data foreach of the Self-Organizing Map candidates, and selects aSelf-Organizing Map candidate the average and the standard deviation ofwhich are both minimum as the Self-Organizing Map.
 5. An informationprocessor according to claim 4, wherein, if there is no Self-OrganizingMap candidate the average and the standard deviation of which are bothminimum, said Self-Organizing Map creating means selects aSelf-Organizing Map candidate the average of which is minimum as theSelf-Organizing Map.
 6. An information processor according to claims 3,wherein said Self-Organizing Map creating means deletes a neuron whichhas never become a winning neuron among neurons in the Self-OrganizingMap that has been selected.
 7. A state judging unit for judging a stateof an object comprising: a storage unit for storing individualseparation models in the form of the plural of the Self-Organizing Maps,created one for each of the plurality of operation modes by aninformation processor defined in claims 1; said detecting means; andjudging means for judging which operation mode an operation of theobject corresponds to based on a relative distance between a detectiondata point in 2n dimension corresponding to detection data obtained bysaid detecting means in real time and a winning neuron in each of saidplural Self-Organizing Maps.
 8. A state judging unit according to claim7, wherein said detecting means calculates the relative distance bydividing the distance between the detection data point obtained by saiddetecting means in real time and the winning neuron in each saidSelf-Organizing Map by the average of distances of the wining neurons inthe Self-Organizing Map to the learning data point used in the processof creating each said Self-Organizing Map in the information processor.9. A state judging unit according to claim 7, wherein said judging meansjudges that, if the relative distance of one of said pluralSelf-Organizing Maps is equal to or smaller than a predeterminedthreshold value, the detection data point conform with the oneSelf-Organizing Map, and that, if the relative distance of saidSelf-Organizing Map is larger than the threshold value, the detectiondata point does not conform with said one Self-Organizing Map.
 10. Adiagnostic unit, including a state judging unit defined in claims 7, fordiagnosing the object, wherein the object is a machine including aconstruction machine, and the plural operation modes represent aparticular operation performed by said machine.
 11. An informationprocessing method comprising the steps of: detecting a multiplicity ofcombinations of n parameter values, where n is a natural number, foreach of a plurality of operation modes in which an object functions,which values vary with operation; and creating a Self-Organizing Map byusing detection data, obtained on the basis of the multiple combinationsof parameter values detected in said step of detecting, as learningdata; wherein, in said step of Self-Organizing-Map creating, a pluralityof the Self-Organizing Maps, serving as individual separation models,are created one for each of the plurality of operation modes.
 12. Aninformation processing method according to claim 11, further comprisingthe step of, between said step of detecting and said step ofSelf-Organizing-Map creating, calculating n time-difference values byprocessing the n parameter values detected in said step of detecting,the Self-Organizing Map being created based on 2n-dimensional dataincluding the n parameter values, which have been detected and whichindicate a momentary state of the object, and the n time-differencevalues which have been calculated using the n parameter values and whichindicate a variation in the momentary stat of the object.
 13. Aninformation processing method according to claim 11, wherein: themultiple combinations of n parameter values are detected in said step ofdetecting; and said step of Self-Organizing-Map includes the sub-stepsof creating a Self-Organizing Map candidate by initially arranging apredetermined number of neurons at random in a 2n-dimensional space,carrying out training regarding a point of the detection data in the2n-dimensional space as a learning data point and creating aSelf-Organizing Map candidate regarding a neuron having a minimumdistance to the learning data point as a winning neuron, and selecting,from two or more Self-Organizing Map candidates created by carrying outsaid step of creating a Self-Organizing Map candidate a number of times,a Self-Organizing Map candidate which has a characteristic closest tothat of the learning data as the Self-Organizing Map.
 14. An informationprocessing method according to claim 13, wherein said step ofSelf-Organizing-Map creating further includes a sub-step of, after saidsub-step of selecting a Self-Organizing Map, deleting an idling neuronwhich has never become a winning neuron among neurons in theSelf-Organizing Map that has been selected.
 15. An informationprocessing method according to claims 11, wherein: when aSelf-Organizing Map for a new operation mode of the object other thanthe plural operation modes is added, the n parameter values are detectedby said step of detecting while the object is functioning in the newoperation mode by said step of detecting; and a Self-Organizing Map forthe new operation mode is created regarding detection data based on amultiplicity of combinations of the parameter values that have beendetected as learning data by said step of Self-Organizing-Map creating.16. A state judging method for judging which operation mode an operationof the object corresponds to using a plurality of Self-Organizing Maps,serving as individual separation models and created one for each of aplurality of operation modes by an information processing methodaccording to claims 11, comprising the step of: detecting the nparameter values that vary with operation; and judging which operationmode an operation of the object corresponds to based on a relativedistance between a detection data point in a 2n-dimensional spacecorresponding to detection data obtained in real time in said step ofdetecting and a winning neuron in each of the plural Self-OrganizingMaps.
 17. A state judging method according to claim 16, furthercomprising the step of, between said step of detecting and said step ofjudging, calculating n time-difference values by processing the nparameter values detected in said step of detecting, the operation modeof the object is judged based on 2n-dimensional data including the nparameter values, which have been detected and which indicate amomentary state of the object, and the n time-difference values, whichhave been processing the n parameter values detected in said step ofdetecting and which indicate a variation in the momentary state of theobject, in said step of judging.
 18. A state judging method according toclaim 17, wherein, said step of judging comprising obtaining therelative distance by dividing the distance between the detection datapoint obtained in real time in said step of detecting and the winningneuron in the Self-Organizing Map by the average of distances of thewining neurons in each said Self-Organizing Map to the learning datapoint used in the process of creating the Self-Organizing Map carriedout by the information processor, if the relative distance of each saidthe plural Self-Organizing Maps is equal to or smaller than apredetermined threshold value, judging the detection data point toconform with the last-named Self-Organizing Map, and if the relativedistance of each said Self-Organizing Map is larger than the thresholdvalue, judging the detection data point not to conform with said oneSelf-Organizing Map.
 19. A diagnosing method, including a state judgingmethod defined in claims 16, for diagnosing the object wherein theobject is a machine including a construction machine, and the pluraloperation modes represent a particular operation performed by saidmachine.
 20. A diagnosing method according to claim 19, wherein, ifthere is no Self-Organizing Map conforming, the particular operation isjudged to be an unknown mode or an abnormal mode in said step ofjudging.